

import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_pytesseract_available():
	from PIL import Image

	from transformers import LayoutLMvaImageProcessor



class        A__(unittest.TestCase							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=7  ,      _lowercase=3  ,      _lowercase=18  ,      _lowercase=30  ,      _lowercase=400  ,      _lowercase=True  ,      _lowercase=None  ,      _lowercase=True  ,      )     -> Optional[int]:
									a_						:						Optional[Any] 							= size if size is not None else {"""height""": 18, """width""": 18}
									a_						:						Optional[int] 							= parent
									a_						:						Dict 							= batch_size
									a_						:						Union[str, Any] 							= num_channels
									a_						:						Optional[int] 							= image_size
									a_						:						str 							= min_resolution
									a_						:						Optional[Any] 							= max_resolution
									a_						:						List[Any] 							= do_resize
									a_						:						str 							= size
									a_						:						List[Any] 							= apply_ocr





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}







@require_torch
@require_pytesseract
class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				str       					=			LayoutLMvaImageProcessor if is_pytesseract_available() else None





						def 			UpperCamelCase__					(    self    )     -> Any:
									a_						:						Union[str, Any] 							= LayoutLMvaImageProcessingTester(self    )





						@property
						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									return self.image_processor_tester.prepare_image_processor_dict()





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Optional[Any] 							= self.image_processing_class(**self.image_processor_dict    )
									self.assertTrue(hasattr(_lowercase  ,      """do_resize"""    )    )
									self.assertTrue(hasattr(_lowercase  ,      """size"""    )    )
									self.assertTrue(hasattr(_lowercase  ,      """apply_ocr"""    )    )





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						str 							= self.image_processing_class.from_dict(self.image_processor_dict    )
									self.assertEqual(image_processor.size  ,      {"""height""": 18, """width""": 18}    )

									a_						:						List[str] 							= self.image_processing_class.from_dict(self.image_processor_dict  ,      size=42    )
									self.assertEqual(image_processor.size  ,      {"""height""": 42, """width""": 42}    )





						def 			UpperCamelCase__					(    self    )     -> str:
									pass





						def 			UpperCamelCase__					(    self    )     -> str:
									# Initialize image_processing
									a_						:						Union[str, Any] 							= self.image_processing_class(**self.image_processor_dict    )
									# create random PIL images
									a_						:						Union[str, Any] 							= prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=_lowercase    )
									for image in image_inputs:
												self.assertIsInstance(_lowercase  ,      Image.Image    )

									# Test not batched input
									a_						:						Dict 							= image_processing(image_inputs[0]  ,      return_tensors="""pt"""    )
									self.assertEqual(
									    encoding.pixel_values.shape  ,      (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,      )

									self.assertIsInstance(encoding.words  ,      _lowercase    )
									self.assertIsInstance(encoding.boxes  ,      _lowercase    )

									# Test batched
									a_						:						Union[str, Any] 							= image_processing(_lowercase  ,      return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,      )





						def 			UpperCamelCase__					(    self    )     -> Any:
									# Initialize image_processing
									a_						:						List[str] 							= self.image_processing_class(**self.image_processor_dict    )
									# create random numpy tensors
									a_						:						Union[str, Any] 							= prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=_lowercase  ,      numpify=_lowercase    )
									for image in image_inputs:
												self.assertIsInstance(_lowercase  ,      np.ndarray    )

									# Test not batched input
									a_						:						Dict 							= image_processing(image_inputs[0]  ,      return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,      (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,      )

									# Test batched
									a_						:						Any 							= image_processing(_lowercase  ,      return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,      )





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									# Initialize image_processing
									a_						:						List[Any] 							= self.image_processing_class(**self.image_processor_dict    )
									# create random PyTorch tensors
									a_						:						Any 							= prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=_lowercase  ,      torchify=_lowercase    )
									for image in image_inputs:
												self.assertIsInstance(_lowercase  ,      torch.Tensor    )

									# Test not batched input
									a_						:						int 							= image_processing(image_inputs[0]  ,      return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,      (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,      )

									# Test batched
									a_						:						Dict 							= image_processing(_lowercase  ,      return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,      )





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									# with apply_OCR = True
									a_						:						Optional[Any] 							= LayoutLMvaImageProcessor()

									from datasets import load_dataset

									a_						:						Union[str, Any] 							= load_dataset("""hf-internal-testing/fixtures_docvqa"""  ,      split="""test"""    )

									a_						:						Any 							= Image.open(ds[0]["""file"""]    ).convert("""RGB"""    )

									a_						:						Optional[int] 							= image_processing(_lowercase  ,      return_tensors="""pt"""    )

									self.assertEqual(encoding.pixel_values.shape  ,      (1, 3, 224, 224)    )
									self.assertEqual(len(encoding.words    )  ,      len(encoding.boxes    )    )

									# fmt: off
									# the words and boxes were obtained with Tesseract 4.1.1
									a_						:						Any 							= [["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]]  # noqa: E231
									a_						:						int 							= [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
									# fmt: on

									self.assertListEqual(encoding.words  ,      _lowercase    )
									self.assertListEqual(encoding.boxes  ,      _lowercase    )

									# with apply_OCR = False
									a_						:						Tuple 							= LayoutLMvaImageProcessor(apply_ocr=_lowercase    )

									a_						:						int 							= image_processing(_lowercase  ,      return_tensors="""pt"""    )

									self.assertEqual(encoding.pixel_values.shape  ,      (1, 3, 224, 224)    )





__snake_case				:      int   		=			[
    """Audio""",
    """Array2D""",
    """Array3D""",
    """Array4D""",
    """Array5D""",
    """ClassLabel""",
    """Features""",
    """Sequence""",
    """Value""",
    """Image""",
    """Translation""",
    """TranslationVariableLanguages""",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages


